Constraints on the extreme mass-ratio inspiral population from LISA data

TL;DR

This paper introduces a hierarchical Bayesian framework utilizing neural network emulators to rapidly analyze LISA data for constraining the population parameters of extreme mass-ratio inspirals, enhancing our understanding of black hole evolution.

Contribution

The authors develop a fast, scalable Bayesian inference method with neural network emulators for EMRI population analysis using LISA data, accounting for selection biases.

Findings

The framework can evaluate detectability of ~10^5 EMRIs in seconds.

Validation on a phenomenological model shows accurate parameter constraints.

Enables detailed studies of black hole mass spectra and formation channels.

Abstract

Gravitational waves from extreme mass-ratio inspirals (EMRIs), the inspirals of stellar-mass compact objects into massive black holes, are predicted to be observed by the Laser Interferometer Space Antenna (LISA). A sufficiently large number of EMRI observations will provide unique insights into the massive black hole population. We have developed a hierarchical Bayesian inference framework capable of constraining the parameters of the EMRI population, accounting for selection biases. We leverage the capacity of a feed-forward neural network as an emulator, enabling detectability calculations of $\sim10^5$ EMRIs in a fraction of a second, speeding up the likelihood evaluation by $\gtrsim6$ orders of magnitude. We validate our framework on a phenomenological EMRI population model. This framework enables studies of how well we can constrain EMRI population parameters, such as the slope of both the massive and stellar-mass black hole mass spectra and the branching fractions of different formation channels, allowing further investigation into the evolution of massive black holes.